Modeling and targeting of erythroleukemia by hematopoietic genome editing
Bibliographic record
Abstract
Acute erythroid leukemia (AEL) is characterized by a distinct morphology, mutational spectrum, lack of preclinical models, and poor prognosis. Here, using multiplexed genome editing of mouse hematopoietic stem and progenitor cells and transplant assays, we developed preclinical models of AEL and non-erythroid acute leukemia and describe the central role of mutational cooperativity in determining leukemia lineage. Different combination of mutations in Trp53, Bcor, Dnmt3a, Rb1, and Nfix resulted in the development of leukemia with an erythroid phenotype, accompanied by the acquisition of alterations in signaling and transcription factor genes that recapitulate human AEL by cross-species genomic analysis. Clonal expansion during tumor evolution was driven by mutational cooccurrence, with clones harboring a higher number of founder and secondary lesions (eg, mutations in signaling genes) showing greater evolutionary fitness. Mouse and human AEL exhibited deregulation of genes regulating erythroid development, notably Gata1, Klf1, and Nfe2, driven by the interaction of mutations of the epigenetic modifiers Dnmt3a and Tet2 that perturbed methylation and thus expression of lineage-specific transcription factors. The established mouse leukemias were used as a platform for drug screening. Drug sensitivity was associated with the leukemia genotype, with the poly (ADP-ribose) polymerase inhibitor talazoparib and the demethylating agent decitabine efficacious in Trp53/Bcor-mutant AEL, CDK7/9 inhibitors in Trp53/Bcor/Dnmt3a-mutant AEL, and gemcitabine and bromodomain inhibitors in NUP98-KDM5A leukemia. In conclusion, combinatorial genome editing has shown the interplay of founding and secondary genetic alterations in phenotype and clonal evolution, epigenetic regulation of lineage-specific transcription factors, and therapeutic tractability in erythroid leukemogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".